Codebook Online No Sharing Consent

knitr::opts_chunk$set(
  warning = TRUE, # show warnings during codebook generation
  message = TRUE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
pander::panderOptions("table.split.table", Inf)

library(codebook)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

#codebook_data <- codebook::bfi
# to import an SPSS file from the same folder uncomment and edit the line below
# codebook_data <- rio::import("mydata.sav")
# for Stata
# codebook_data <- rio::import("mydata.dta")
# for CSV
codebook_data <- rio::import("online_data_non_shared_condition.csv")
#drop <- c("V1", "submitdate", "startlanguage", "click")
#codebook_data = codebook_data[ , !(names(codebook_data) %in% drop)]
# omit the following lines, if your missing values are already properly labelled
codebook_data <- detect_missing(codebook_data,
    only_labelled = TRUE, # only labelled values are autodetected as
                                   # missing
    negative_values_are_missing = FALSE, # negative values are missing values
    ninety_nine_problems = TRUE   # 99/999 are missing values, if they
                                   # are more than 5 MAD from the median
    )

# If you are not using formr, the codebook package needs to guess which items
# form a scale. The following line finds item aggregates with names like this:
# scale = scale_1 + scale_2R + scale_3R
# identifying these aggregates allows the codebook function to
# automatically compute reliabilities.
# However, it will not reverse items automatically.
#codebook_data <- detect_scales(codebook_data)

var_label(codebook_data) <- list(
  id = "ID variable from raw data", 
  lastpage = "Last page completed by the participant, page 12 and 13 are considered as full participation", 
  random = "Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor.", 
  cb = "No data sharing policy consent presented. One participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. See manuscript for details", 
  ca = "Data sharing policy presented", 
  mc_1 = "comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes?", 
  mc_2 = "comprehension question consent 2 (distractor): Is your data anonymous?", 
  mc_3 = "comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared?", 
  mc_4 = "comprehension question consent 3 (distractor): Can you stop your participation at any time?", 
  bf_1 = "TIPI item 1, Extraversion: I see myself as extraverted, enthousiastic.", 
  bf_2 = "TIPI item 2, Agreeableness: I see myself as critical, quarrelsome.", 
  bf_3 = "TIPI item 3, Conscientiousness: I see myself as dependable, self-disciplined.", 
  cr_1 = "Careless response item 1: I am using an electronic device at this moment.", 
  bf_5 = "TIPI item 4, Neuroticsm: I see myself as anxious, easily upset.", 
  bf_6 = "TIPI item 5, Openness to experience: I see myself as open to new experiences, complex.", 
  bf_7 = "TIPI item 6, Extraversion: I see myself as reserved, quiet.", 
  bf_8 = "TIPI item 7, Agreeableness: I see myself as sympathetic, warm.", 
  cr_2 = "Careless response item 2: I turn into a leprechaun at night.", 
  bf_10 = "TIPI item 8, Conscientiousness: I see myself as disorganized, careless.", 
  bf_11 = "TIPI item 9, Neuroticsm: I see myself as calm, emotionally stable.", 
  bf_12 = "TIPI item 10, Openness to experience: I see myself as conventional, uncreative.", 
  soc_d_1 = "Social desirability questionnaire item 1: Before voting I thoroughly investigate the qualifications of all the candidates.",
  soc_d_2 = "Social desirability questionnaire item 2: I never hesitate to go out of my way to help someone in trouble.", 
  soc_d_3 = "Social desirability questionnaire item 3: It is sometimes hard for me to go on with my work if I am not encouraged.", 
  soc_d_4 = "Social desirability questionnaire item 4: I have never intensely disliked anyone.", 
  soc_d_5 = "Social desirability questionnaire item 5: On occasion I have had doubts about my ability to succeed in life.", 
  soc_d_6 = "Social desirability questionnaire item 6: I sometimes feel resentful when I don't get my way.", 
  cr_3 = "Careless response item 3: All my friends are aliens.", 
  soc_d_7 = "Social desirability questionnaire item 7: I am always careful about my manner of dress.", 
  soc_d_8 = "Social desirability questionnaire item 8: My table manners at home are as good as when I eat out in a restaurant.", 
  soc_d_9 = "Social desirability questionnaire item 9: If I could get into a movie without paying and be sure I was not seen I would probably do it.", 
  soc_d_10 = "Social desirability questionnaire item 10: On a few occasions, I have given up doing something because I thought too little of my ability.", 
  soc_d_11 = "Social desirability questionnaire item 11: I like to gossip at times.", 
  soc_d_12 = "Social desirability questionnaire item 12: There have been times when I felt like rebelling against people in authority even though I knew they were right.", 
  cr_4 = "Careless response item 4: All my friends say I would make a great poodle.", 
  soc_d_13 = "Social desirability questionnaire item 13: No matter who I'm talking to, I'm always a good listener.", 
  soc_d_14 = "Social desirability questionnaire item 14: I can remember 'playing sick' to get out of something.", 
  soc_d_15 = "Social desirability questionnaire item 15: There have been occasions when I took advantage of someone.", 
  soc_d_16 = "Social desirability questionnaire item 16: I'm always willing to admit it when I make a mistake.", 
  soc_d_17 = "Social desirability questionnaire item 17: I always try to practice what I preach.", 
  soc_d_18 = "Social desirability questionnaire item 18: I don't find it particularly difficult to get along with loud mouthed, obnoxious people.", 
  cr_5 = "Careless response item 5: I eat cement occasionally.", 
  soc_d_19 = "Social desirability questionnaire item 19: I sometimes try to get even rather than forgive and forget.", 
  soc_d_20 = "Social desirability questionnaire item 20: When I don't know something I don't at all mind admitting it.", 
  soc_d_21 = "Social desirability questionnaire item 21: I am always courteous, even to people who are disagreeable.", 
  soc_d_22 = "Social desirability questionnaire item 22: At times I have really insisted on having things my own way.", 
  soc_d_23 = "Social desirability questionnaire item 23: There have been occasions when I felt like smashing things.", 
  soc_d_24 = "Social desirability questionnaire item 24: I would never think of letting someone else be punished for my wrong- doings.", 
  soc_d_25 = "Social desirability questionnaire item 25: I never resent being asked to return a favor.", 
  soc_d_26 = "Social desirability questionnaire item 26: I have never been irked when people expressed ideas very different from my own.", 
  cr_6 = "Careless response item 6: I can teleport across time and space.", 
  soc_d_27 = "Social desirability questionnaire item 27: I never make a long trip without checking the safety of my car.", 
  soc_d_28 = "Social desirability questionnaire item 28: There have been times when I was quite jealous of the good fortune of others.", 
  soc_d_29 = "Social desirability questionnaire item 29: I have almost never felt the urge to tell someone off.", 
  soc_d_30 = "Social desirability questionnaire item 30: I am sometimes irritated by people who ask favors of me. ", 
  soc_d_31 = "Social desirability questionnaire item 31: I have never felt that I was punished without cause.", 
  cr_7 = "Careless response item 7: I will be punished for meeting the requirements of my job.", 
  soc_d_32 = "Social desirability questionnaire item 32: I sometimes think when people have a misfortune they only got what they deserved.", 
  soc_d_33 = "Social desirability questionnaire item 33: I have never deliberately said something that hurt someone's feelings.", 
  everesthigh = "Anchoring paradigm, high anchor: Height of Mount Everest", 
  chicagohigh = "Anchoring paradigm, high anchor: Population of Chicago", 
  babieshigh = "Anchoring paradigm, high anchor: Babies born each day", 
  everestlow = "Anchoring paradigm, low anchor: Height of Mount Everest", 
  chicagolow = "Anchoring paradigm, low anchor: Population of Chicago", 
  babieslow = "Anchoring paradgim, low anchor: Babies born each day", 
  d1 = "NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained?", 
  d2.sq001 = "NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : Yes, I remember", 
  d2.sq002 = "NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : No, I don't remember", 
  d3.sq001 = "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': Yes
", 
  d3.sq002 = "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': No", 
  d3.sq003 = "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': I don't remember", 
  gender = "Gender: open-entry self-report", 
  age = "Age categories", 
  consent = "Variables cb and ca combined in one variable", 
  cond_anc = "Anchoring condition: high and low", 
  refused = "Refusal to participate, one participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. This participant was in the 'no data sharing' condition.", 
  remember = "Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy?", 
  anc_baby = "Aggregated anchoring response, combining variables babieshigh and babieslow in one variable", 
  anc_everest = "Aggregated anchoring response, combining variables everesthigh and everestlow in one variable", 
  anc_chicago = "Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable", 
  gender_r = "Gender variable cleaned for grammar, language variations and orthography"
       )

val_labels(codebook_data$remember) <- c("No or wrong memory" = 0, "correct memory" = 1)

add_likert_labels <- function(x) {
   val_labels(x) <- c("No" = 0, 
                      "Yes" = 1)
   x
 }
likert_items <- names(codebook_data[, c("soc_d_1", "soc_d_2", "soc_d_3", "soc_d_4", "soc_d_5", "soc_d_6", "soc_d_7", "soc_d_8", "soc_d_9", "soc_d_10", "soc_d_11", "soc_d_12", "soc_d_13", "soc_d_14", "soc_d_15", "soc_d_16", "soc_d_17", "soc_d_18", "soc_d_19", "soc_d_20", "soc_d_21", "soc_d_22", "soc_d_23", "soc_d_24", "soc_d_25", "soc_d_26", "soc_d_27", "soc_d_28", "soc_d_29", "soc_d_30", "soc_d_31", "soc_d_32", "soc_d_33") ])
codebook_data <- codebook_data %>% mutate_at(likert_items, add_likert_labels)

add_likert_labels <- function(x) {
   val_labels(x) <- c("Yes" = 0, 
                      "No" = 1)
   x
 }
likert_items <- names(codebook_data[, c("cr_1", "cr_2", "cr_3", "cr_4", "cr_5", "cr_6", "cr_7") ])
codebook_data <- codebook_data %>% mutate_at(likert_items, add_likert_labels)


add_likert_labels <- function(x) {
   val_labels(x) <- c("Disagree strongly" = 1,
                      "Disagree moderately" = 2,
                      "Disagree a little" = 3,
                      "Neither agree nor disagree" = 4,
                      "Agree a little" = 5,
                      "Agree moderately" = 6,
                      "Agree strongly" = 7) 
   x
 }
likert_items <- names(codebook_data[, c("bf_1", "bf_2",
                                        "bf_3", "bf_5", "bf_6",
                                        "bf_7", "bf_8", "bf_10", 
                                        "bf_11", "bf_12") ])
codebook_data <- codebook_data %>% mutate_at(likert_items, add_likert_labels)

####     Extraversion     ####
codebook_data$Extraversion <- codebook_data %>% select("bf_1", "bf_7") %>% aggregate_and_document_scale()

reversed_items <- c("bf_7")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Extraversion <- codebook_data %>% select("bf_1", "bf_7R") %>% aggregate_and_document_scale()

####    Agreeableness    ####
codebook_data$Agreeableness <- codebook_data %>% select("bf_2", "bf_8") %>% aggregate_and_document_scale()

reversed_items <- c("bf_2")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Agreeableness <- codebook_data %>% select("bf_2R", "bf_8") %>% aggregate_and_document_scale()

####    Conscientiousness    ####

codebook_data$Conscientiousness <- codebook_data %>% select("bf_3", "bf_10") %>% aggregate_and_document_scale()

reversed_items <- c("bf_10")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Conscientiousness <- codebook_data %>% select("bf_3", "bf_10R") %>% aggregate_and_document_scale()

####    Neuroticism    ####
codebook_data$Neuroticism <- codebook_data %>% select("bf_5", "bf_11") %>% aggregate_and_document_scale()

reversed_items <- c("bf_5")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Neuroticism <- codebook_data %>% select("bf_5R", "bf_11") %>% aggregate_and_document_scale()

####    Openness to experience    ####

codebook_data$'Openness to experience' <- codebook_data %>% select("bf_6", "bf_12") %>% aggregate_and_document_scale()

reversed_items <- c("bf_12")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$'Openness to experience' <- codebook_data %>% select("bf_6", "bf_12R") %>% aggregate_and_document_scale()

####    Social Desirability    ####
codebook_data$'Social Desirability' <- codebook_data %>% select("soc_d_1", "soc_d_2", "soc_d_3", 
         "soc_d_4", "soc_d_5", "soc_d_6", 
         "soc_d_7", "soc_d_8", "soc_d_9", 
         "soc_d_10", "soc_d_11", "soc_d_12", 
         "soc_d_13", "soc_d_14", "soc_d_15", 
         "soc_d_16", "soc_d_17", "soc_d_18", 
         "soc_d_19", "soc_d_20", "soc_d_21", 
         "soc_d_22", "soc_d_23", "soc_d_24", 
         "soc_d_25", "soc_d_26", "soc_d_27", 
         "soc_d_28", "soc_d_29", "soc_d_30", 
         "soc_d_31", "soc_d_32", "soc_d_33") %>% aggregate_and_document_scale()

reversed_items <- c("soc_d_3", "soc_d_5", "soc_d_6", "soc_d_9",
                    "soc_d_10", "soc_d_11","soc_d_12","soc_d_14",
                    "soc_d_15", "soc_d_19", "soc_d_22", "soc_d_23",
                    "soc_d_28", "soc_d_30", "soc_d_32"
                    )
codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$'Social Desirability' <- codebook_data %>% select("soc_d_1", "soc_d_2", "soc_d_3R", 
         "soc_d_4", "soc_d_5R", "soc_d_6R", 
         "soc_d_7", "soc_d_8", "soc_d_9R", 
         "soc_d_10R", "soc_d_11R", "soc_d_12R", 
         "soc_d_13", "soc_d_14R", "soc_d_15R", 
         "soc_d_16", "soc_d_17", "soc_d_18", 
         "soc_d_19R", "soc_d_20", "soc_d_21", 
         "soc_d_22R", "soc_d_23R", "soc_d_24", 
         "soc_d_25", "soc_d_26", "soc_d_27", 
         "soc_d_28R", "soc_d_29", "soc_d_30R", 
         "soc_d_31", "soc_d_32R", "soc_d_33") %>% aggregate_and_document_scale()

codebook_data$'Careless responses' <- codebook_data %>% select("cr_1", "cr_2", "cr_3", 
         "cr_4", "cr_5", "cr_6", 
         "cr_7") %>% aggregate_and_document_scale()
reversed_items <- c("cr_1")
codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$'Careless responses' <- codebook_data %>% select("cr_1R", "cr_2", "cr_3", 
         "cr_4", "cr_5", "cr_6", 
         "cr_7") %>% aggregate_and_document_scale()


metadata(codebook_data)$name <- "Online (Prolific.co) data on TIPI, Social Desirability, Careless Response and Anchoring Paradigm, confidential data set"
metadata(codebook_data)$description <- "10 items taking from the Very brief measure of the Big 5 Personality questionnaire (Gosling, Rentfrow, & Swann, 2003), 33 items from the Social desirability scale (Crowne & Marlowe, 1960) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012). This dataset cannot be publicly shared, as the study consent for the participants represented in this data set stated that we would not provide public access to the data. If you are interested in re-analyzing the entire dataset, please contact the authors. Please find the shareable half of the data set on our osf.io page (see doi)"
metadata(codebook_data)$identifier <- "https://doi.org/10.17605/OSF.IO/AM6BC"
metadata(codebook_data)$creator <- "Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein"
metadata(codebook_data)$citation <- "Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Data collected online. https://doi.org/10.17605/OSF.IO/AM6BC"
metadata(codebook_data)$datePublished <- "2019-08-06"
metadata(codebook_data)$temporalCoverage <- "2019-06-17 to 2019-06-21" 
metadata(codebook_data)$spatialCoverage <- "Online participants residing in, or citizens of, the EU at time of data collection" 
#rio::export(codebook_data, "offline_data_shared.rds")

codebook(codebook_data)

## Warning in doTryCatch(return(expr), name, parentenv, handler): Reliability
## CIs could not be computed for Careless responses

## Warning in doTryCatch(return(expr), name, parentenv, handler): missing
## value where TRUE/FALSE needed

knitr::asis_output(data_info)

Metadata

Description

if (exists("name", meta)) {
  glue::glue(
    "__Dataset name__: {name}",
    .envir = meta)
}

Dataset name: Online (Prolific.co) data on TIPI, Social Desirability, Careless Response and Anchoring Paradigm, confidential data set

cat(description)

10 items taking from the Very brief measure of the Big 5 Personality questionnaire (Gosling, Rentfrow, & Swann, 2003), 33 items from the Social desirability scale (Crowne & Marlowe, 1960) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012). This dataset cannot be publicly shared, as the study consent for the participants represented in this data set stated that we would not provide public access to the data. If you are interested in re-analyzing the entire dataset, please contact the authors. Please find the shareable half of the data set on our osf.io page (see doi)

Metadata for search engines

• Temporal Coverage: 2019-06-17 to 2019-06-21
• Spatial Coverage: Online participants residing in, or citizens of, the EU at time of data collection

• Citation: Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Data collected online. https://doi.org/10.17605/OSF.IO/AM6BC

• Identifier: https://doi.org/10.17605/OSF.IO/AM6BC

• Date published: 2019-08-06

• Creator:Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein

meta <- meta[setdiff(names(meta),
                     c("creator", "datePublished", "identifier",
                       "url", "citation", "spatialCoverage", 
                       "temporalCoverage", "description", "name"))]
pander::pander(meta)

• keywords: V1, id, consent, cond_anc, refused, remember, anc_baby, anc_everest, anc_chicago, gender_r, oq, bf_1, bf_2R, bf_3, bf_5R, bf_6, bf_7R, bf_8, bf_10R, bf_11, bf_12R, soc_d_1, soc_d_2, soc_d_3R, soc_d_4, soc_d_5R, soc_d_6R, soc_d_7, soc_d_8, soc_d_9R, soc_d_10R, soc_d_11R, soc_d_12R, soc_d_13, soc_d_14R, soc_d_15R, soc_d_16, soc_d_17, soc_d_18, soc_d_19R, soc_d_20, soc_d_21, soc_d_22R, soc_d_23R, soc_d_24, soc_d_25, soc_d_26, soc_d_27, soc_d_28R, soc_d_29, soc_d_30R, soc_d_31, soc_d_32R, soc_d_33, cr_1R, cr_2, cr_3, cr_4, cr_5, cr_6, cr_7, everesthigh, chicagohigh, babieshigh, everestlow, chicagolow, babieslow, d1, d2.sq001, d2.sq002, d3.sq001, d3.sq002, d3.sq003, gender, age, end, return, lastpage, random, cb, ca, mc_1, mc_2, mc_3, mc_4, Extraversion, Agreeableness, Conscientiousness, Neuroticism, Openness to experience, Social Desirability and Careless responses

knitr::asis_output(survey_overview)

Variables

if (detailed_variables || detailed_scales) {
  knitr::asis_output(paste0(scales_items, sep = "\n\n\n", collapse = "\n\n\n"))
}

V1

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
V1	integer	0	263	263	297.31	171.15	1	149.5	300	445	576	▇▆▇▆▇▇▇▇

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

id

ID variable from raw data

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
id	ID variable from raw data	integer	0	263	263	328.89	192.86	1	161.5	330	495	654	▇▇▇▇▇▇▇▇

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

consent

Variables cb and ca combined in one variable

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
consent	Variables cb and ca combined in one variable	character	0	263	263	0	1	1	1

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

cond_anc

Anchoring condition: high and low

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
cond_anc	Anchoring condition: high and low	character	0	263	263	0	2	1	1

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

refused

Refusal to participate, one participant clicked on ‘I disagree’ but contacted the first author by email to indicate that they had ‘a bug’ and was unable to complete the questionnaire. This participant was in the ‘no data sharing’ condition.

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
refused	Refusal to participate, one participant clicked on ‘I disagree’ but contacted the first author by email to indicate that they had ‘a bug’ and was unable to complete the questionnaire. This participant was in the ‘no data sharing’ condition.	integer	0	263	263	0.0038	0.062	0	0	0	0	1	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

remember

Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy?

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
remember	Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy?	integer	0. No or wrong memory, 1. correct memory	1	262	263	0.73	0.44	0	0	1	1	1	▃▁▁▁▁▁▁▇

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• No or wrong memory: 0
• correct memory: 1

anc_baby

Aggregated anchoring response, combining variables babieshigh and babieslow in one variable

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
anc_baby	Aggregated anchoring response, combining variables babieshigh and babieslow in one variable	numeric	1	262	263	77613.63	4e+05	1.8	1000	10000	36750	4e+06	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

anc_everest

Aggregated anchoring response, combining variables everesthigh and everestlow in one variable

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
anc_everest	Aggregated anchoring response, combining variables everesthigh and everestlow in one variable	numeric	1	262	263	8981.49	8176.08	8	6967	8500	9000	84000	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

anc_chicago

Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
anc_chicago	Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable	numeric	1	262	263	4.52	6.27	1e-04	2	3	5	50	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

gender_r

Gender variable cleaned for grammar, language variations and orthography

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

3 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
gender_r	Gender variable cleaned for grammar, language variations and orthography	character	3	260	263	0	3	4	10

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

oq

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

33 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	data_type	missing	complete	n	empty	n_unique	min	max
oq	character	33	230	263	0	227	2	897

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

everesthigh

Anchoring paradigm, high anchor: Height of Mount Everest

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

137 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
everesthigh	Anchoring paradigm, high anchor: Height of Mount Everest	numeric	137	126	263	11573.93	9024.16	8.85	8650	8900	13337.5	84000	▇▃▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

chicagohigh

Anchoring paradigm, high anchor: Population of Chicago

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

137 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
chicagohigh	Anchoring paradigm, high anchor: Population of Chicago	numeric	137	126	263	313337.14	1153850.61	1.3	3	3	7	7e+06	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

babieshigh

Anchoring paradigm, high anchor: Babies born each day

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

137 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
babieshigh	Anchoring paradigm, high anchor: Babies born each day	numeric	137	126	263	110755.6	455097.97	1.8	10000	30000	50000	4e+06	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

everestlow

Anchoring paradigm, low anchor: Height of Mount Everest

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

127 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
everestlow	Anchoring paradigm, low anchor: Height of Mount Everest	numeric	127	136	263	6579.68	6461.63	8	2000	8000	8533	60000	▇▇▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

chicagolow

Anchoring paradigm, low anchor: Population of Chicago

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

127 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
chicagolow	Anchoring paradigm, low anchor: Population of Chicago	numeric	127	136	263	409740.82	4297754.77	0.1	1.2	2.6	5.05	5e+07	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

babieslow

Anchoring paradgim, low anchor: Babies born each day

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

127 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
babieslow	Anchoring paradgim, low anchor: Babies born each day	integer	127	136	263	46908.57	341493.94	10	230	1000	10000	3853000	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

d1

NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained?

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
d1	NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained?	character	0	263	263	0	3	2	3

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

d2.sq001

NOT USED Answer option to control question d2: ‘Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?’ : Yes, I remember

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
d2.sq001	NOT USED Answer option to control question d2: ‘Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?’ : Yes, I remember	character	0	263	263	0	3	2	3

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

d2.sq002

NOT USED Answer option to control question d2: ‘Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?’ : No, I don’t remember

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
d2.sq002	NOT USED Answer option to control question d2: ‘Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?’ : No, I don’t remember	character	0	263	263	0	3	2	3

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

d3.sq001

NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: Yes

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
d3.sq001	NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: Yes	character	0	263	263	0	3	2	3

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

d3.sq002

NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: No

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
d3.sq002	NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: No	character	0	263	263	0	3	2	3

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

d3.sq003

NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: I don’t remember

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
d3.sq003	NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: I don’t remember	character	0	263	263	0	3	2	3

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

gender

Gender: open-entry self-report

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

2 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
gender	Gender: open-entry self-report	character	2	261	263	0	11	1	11

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

age

Age categories

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

2 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
age	Age categories	character	2	261	263	0	8	13	13

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

end

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

237 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	data_type	missing	complete	n	empty	n_unique	min	max
end	character	237	26	263	0	26	1	228

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

return

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

## Error in if (stats::median(table(x)) == 1) {: missing value where TRUE/FALSE needed

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

## No non-missing values to show.

knitr::opts_chunk$set(fig.height = old_height)

263 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	data_type	missing	complete	n	count	mean
return	logical	263	0	263	263	NaN

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

lastpage

Last page completed by the participant, page 12 and 13 are considered as full participation

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
lastpage	Last page completed by the participant, page 12 and 13 are considered as full participation	integer	0	263	263	12.19	0.39	12	12	12	12	13	▇▁▁▁▁▁▁▂

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

random

Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor.

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
random	Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor.	integer	0	263	263	1.52	0.5	1	1	2	2	2	▇▁▁▁▁▁▁▇

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

cb

No data sharing policy consent presented. One participant clicked on ‘I disagree’ but contacted the first author by email to indicate that they had ‘a bug’ and was unable to complete the questionnaire. See manuscript for details

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
cb	No data sharing policy consent presented. One participant clicked on ‘I disagree’ but contacted the first author by email to indicate that they had ‘a bug’ and was unable to complete the questionnaire. See manuscript for details	character	0	263	263	0	2	7	10

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

ca

Data sharing policy presented

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

## Error in if (stats::median(table(x)) == 1) {: missing value where TRUE/FALSE needed

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

## No non-missing values to show.

knitr::opts_chunk$set(fig.height = old_height)

263 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	count	mean
ca	Data sharing policy presented	logical	263	0	263	263	NaN

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

mc_1

comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes?

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
mc_1	comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes?	character	1	262	263	0	3	2	16

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

mc_2

comprehension question consent 2 (distractor): Is your data anonymous?

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
mc_2	comprehension question consent 2 (distractor): Is your data anonymous?	character	1	262	263	0	2	2	3

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

mc_3

comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared?

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
mc_3	comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared?	character	1	262	263	0	3	2	16

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

mc_4

comprehension question consent 3 (distractor): Can you stop your participation at any time?

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
mc_4	comprehension question consent 3 (distractor): Can you stop your participation at any time?	character	1	262	263	0	3	2	16

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

Scale: Extraversion

Overview

Reliability: .

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Cronbach Alpha	0.7173
Spearman Brown	0.7175

Positive correlations: 1 out of 1 (100%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_1, bf_7R
##               Observations: 262
##      Positive correlations: 1 out of 1 (100%)
## 
## Estimates assuming interval level:
## 
## Spearman Brown coefficient: 0.72
##           Cronbach's alpha: 0.72
##        Pearson Correlation: 0.56
## 
## 
## Eigen values: 1.559, 0.441NULL
## 
##       vars   n mean   sd median trimmed  mad min max range skew kurtosis
## bf_1     1 262 3.77 1.79      4    3.75 1.48   1   7     6 0.09    -1.11
## bf_7R    2 262 3.38 1.73      3    3.29 1.48   1   7     6 0.45    -0.82
##         se
## bf_1  0.11
## bf_7R 0.11

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_1	TIPI item 1, Extraversion: I see myself as extraverted, enthousiastic.	integer	1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly	1	262	263	3.77	1.79	1	2	4	5	7	▅▆▇▅▁▇▅▂
bf_7R	TIPI item 6, Extraversion: I see myself as reserved, quiet.	numeric	7. Disagree strongly, 6. Disagree moderately, 5. Disagree a little, 4. Neither agree nor disagree, 3. Agree a little, 2. Agree moderately, 1. Agree strongly	1	262	263	3.38	1.73	1	2	3	5	7	▅▆▇▂▁▅▃▂

Scale: Agreeableness

Overview

Reliability: .

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Cronbach Alpha	-0.3971
Spearman Brown	-0.4053

Positive correlations: 0 out of 1 (0%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_2R, bf_8
##               Observations: 262
##      Positive correlations: 0 out of 1 (0%)
## 
## Estimates assuming interval level:
## 
## Spearman Brown coefficient: -0.41
##           Cronbach's alpha: -0.4
##        Pearson Correlation: -0.17
## 
## 
## Eigen values: 1.169, 0.831NULL
## 
##       vars   n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_2R    1 262 4.23 1.61      5    4.32 1.48   1   7     6 -0.43    -0.84
## bf_8     2 262 5.32 1.34      6    5.49 1.48   1   7     6 -1.22     1.55
##         se
## bf_2R 0.10
## bf_8  0.08

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_2R	TIPI item 2, Agreeableness: I see myself as critical, quarrelsome.	numeric	1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly	1	262	263	4.23	1.61	1	3	5	5	7	▂▃▃▃▁▇▅▁
bf_8	TIPI item 7, Agreeableness: I see myself as sympathetic, warm.	integer	1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly	1	262	263	5.32	1.34	1	5	6	6	7	▁▁▁▂▁▇▇▃

Scale: Conscientiousness

Overview

Reliability: .

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Cronbach Alpha	0.562
Spearman Brown	0.5651

Positive correlations: 1 out of 1 (100%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_3, bf_10R
##               Observations: 262
##      Positive correlations: 1 out of 1 (100%)
## 
## Estimates assuming interval level:
## 
## Spearman Brown coefficient: 0.57
##           Cronbach's alpha: 0.56
##        Pearson Correlation: 0.39
## 
## 
## Eigen values: 1.394, 0.606NULL
## 
##        vars   n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_3      1 262 5.03 1.40      5    5.13 1.48   1   7     6 -0.72     0.04
## bf_10R    2 262 5.05 1.59      5    5.15 1.48   1   7     6 -0.47    -0.93
##          se
## bf_3   0.09
## bf_10R 0.10

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_3	TIPI item 3, Conscientiousness: I see myself as dependable, self-disciplined.	integer	1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly	1	262	263	5.03	1.4	1	4	5	6	7	▁▁▂▃▁▇▇▃
bf_10R	TIPI item 8, Conscientiousness: I see myself as disorganized, careless.	numeric	7. Disagree strongly, 6. Disagree moderately, 5. Disagree a little, 4. Neither agree nor disagree, 3. Agree a little, 2. Agree moderately, 1. Agree strongly	1	262	263	5.05	1.59	1	4	5	6	7	▁▂▅▃▁▅▇▆

Scale: Neuroticism

Overview

Reliability: .

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Cronbach Alpha	-2.327
Spearman Brown	-2.396

Positive correlations: 0 out of 1 (0%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_5R, bf_11
##               Observations: 262
##      Positive correlations: 0 out of 1 (0%)
## 
## Estimates assuming interval level:
## 
## Spearman Brown coefficient: -2.4
##           Cronbach's alpha: -2.33
##        Pearson Correlation: -0.54
## 
## 
## Eigen values: 1.545, 0.455NULL
## 
##       vars   n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_5R    1 262 3.98 1.82      4    3.94 2.97   1   7     6  0.06    -1.17
## bf_11    2 262 4.68 1.54      5    4.75 1.48   1   7     6 -0.43    -0.69
##         se
## bf_5R 0.11
## bf_11 0.10

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_5R	TIPI item 4, Neuroticsm: I see myself as anxious, easily upset.	numeric	1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly	1	262	263	3.98	1.82	1	2	4	5	7	▂▇▆▃▁▇▅▃
bf_11	TIPI item 9, Neuroticsm: I see myself as calm, emotionally stable.	integer	1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly	1	262	263	4.68	1.54	1	4	5	6	7	▁▂▅▅▁▇▇▃

Scale: Openness to experience

Overview

Reliability: .

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Cronbach Alpha	0.444
Spearman Brown	0.4521

Positive correlations: 1 out of 1 (100%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_6, bf_12R
##               Observations: 262
##      Positive correlations: 1 out of 1 (100%)
## 
## Estimates assuming interval level:
## 
## Spearman Brown coefficient: 0.45
##           Cronbach's alpha: 0.44
##        Pearson Correlation: 0.29
## 
## 
## Eigen values: 1.292, 0.708NULL
## 
##        vars   n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_6      1 262 5.29 1.22      5    5.39 1.48   1   7     6 -0.78     0.85
## bf_12R    2 262 4.79 1.51      5    4.88 1.48   1   7     6 -0.52    -0.56
##          se
## bf_6   0.08
## bf_12R 0.09

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_6	TIPI item 5, Openness to experience: I see myself as open to new experiences, complex.	integer	1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly	1	262	263	5.29	1.22	1	5	5	6	7	▁▁▁▂▁▇▆▃
bf_12R	TIPI item 10, Openness to experience: I see myself as conventional, uncreative.	numeric	7. Disagree strongly, 6. Disagree moderately, 5. Disagree a little, 4. Neither agree nor disagree, 3. Agree a little, 2. Agree moderately, 1. Agree strongly	1	262	263	4.79	1.51	1	4	5	6	7	▁▂▃▅▁▆▇▃

Scale: Social Desirability

Overview

Reliability: ω_ordinal [95% CI] = 0.28 [0.18;0.37].

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.1378
Omega Psych Tot	0.6496
Omega Psych H	0.5475
Omega Ordinal	0.2761
Cronbach Alpha	0.5032
Greatest Lower Bound	0.7487
Alpha Ordinal	0.6437

Positive correlations: 313 out of 528 (59%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: soc_d_1, soc_d_2, soc_d_3R, soc_d_4, soc_d_5R, soc_d_6R, soc_d_7, soc_d_8, soc_d_9R, soc_d_10R, soc_d_11R, soc_d_12R, soc_d_13, soc_d_14R, soc_d_15R, soc_d_16, soc_d_17, soc_d_18, soc_d_19R, soc_d_20, soc_d_21, soc_d_22R, soc_d_23R, soc_d_24, soc_d_25, soc_d_26, soc_d_27, soc_d_28R, soc_d_29, soc_d_30R, soc_d_31, soc_d_32R, soc_d_33
##               Observations: 262
##      Positive correlations: 313 out of 528 (59%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.14
##       Omega (hierarchical): 0.55
##    Revelle's omega (total): 0.65
## Greatest Lower Bound (GLB): 0.75
##              Coefficient H: 0.75
##           Cronbach's alpha: 0.5
## Confidence intervals:
##              Omega (total): [0.03, 0.25]
##           Cronbach's alpha: [0.42, 0.59]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.28
##  Ordinal Omega (hierarch.): 0.12
##   Ordinal Cronbach's alpha: 0.64
## Confidence intervals:
##      Ordinal Omega (total): [0.18, 0.37]
##   Ordinal Cronbach's alpha: [0.58, 0.71]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 3.475, 2.2, 1.683, 1.644, 1.446, 1.355, 1.308, 1.27, 1.165, 1.12, 1.103, 1.013, 0.976, 0.93, 0.926, 0.891, 0.846, 0.797, 0.79, 0.745, 0.707, 0.697, 0.675, 0.651, 0.63, 0.595, 0.586, 0.534, 0.51, 0.484, 0.481, 0.404, 0.363
## Loadings:
##           TC11   TC12   TC1    TC2    TC7    TC5    TC4    TC10   TC8   
## soc_d_1                                       0.147                     
## soc_d_2          -0.183 -0.182  0.114  0.241                0.372  0.294
## soc_d_3R   0.737  0.106  0.154                                          
## soc_d_4          -0.119  0.133         0.265 -0.467               -0.303
## soc_d_5R   0.153         0.200                      -0.140  0.217       
## soc_d_6R                 0.728        -0.170                            
## soc_d_7   -0.168         0.256 -0.240  0.142 -0.243  0.259         0.202
## soc_d_8   -0.497  0.237  0.126  0.296        -0.177                     
## soc_d_9R   0.187  0.473         0.142               -0.218              
## soc_d_10R  0.535                      -0.124  0.141         0.187  0.237
## soc_d_11R -0.192         0.170 -0.259  0.194  0.388         0.280       
## soc_d_12R                0.152                0.707               -0.157
## soc_d_13  -0.228         0.167  0.212        -0.378         0.123       
## soc_d_14R  0.282                0.253 -0.119  0.237  0.168  0.338 -0.258
## soc_d_15R -0.100  0.520  0.400                0.132               -0.180
## soc_d_16                -0.308  0.236                0.411  0.135  0.283
## soc_d_17                              -0.106         0.812        -0.150
## soc_d_18                               0.195  0.223  0.172 -0.171 -0.455
## soc_d_19R  0.106               -0.130 -0.166        -0.110  0.428 -0.106
## soc_d_20          0.193                       0.180  0.592 -0.132  0.297
## soc_d_21   0.179                       0.122 -0.179  0.250 -0.243 -0.183
## soc_d_22R                0.415  0.217         0.211                0.164
## soc_d_23R                                                   0.818       
## soc_d_24   0.104 -0.103         0.110                      -0.156  0.694
## soc_d_25  -0.149                0.773                              0.115
## soc_d_26   0.152        -0.146  0.512                                   
## soc_d_27                                     -0.108  0.103              
## soc_d_28R  0.265  0.170  0.423         0.140  0.150         0.114  0.180
## soc_d_29   0.228 -0.216  0.200  0.303  0.376 -0.222                     
## soc_d_30R  0.226  0.484  0.127 -0.331                0.121         0.131
## soc_d_31          0.192 -0.211         0.731 -0.116                     
## soc_d_32R         0.731         0.106                                   
## soc_d_33         -0.211                0.715  0.166                     
##           TC9    TC3    TC6   
## soc_d_1    0.786        -0.131
## soc_d_2    0.253              
## soc_d_3R                      
## soc_d_4          -0.185 -0.178
## soc_d_5R   0.231 -0.607       
## soc_d_6R  -0.113              
## soc_d_7    0.328  0.220  0.166
## soc_d_8    0.139        -0.178
## soc_d_9R          0.164  0.380
## soc_d_10R  0.112 -0.202 -0.199
## soc_d_11R        -0.166  0.299
## soc_d_12R  0.192              
## soc_d_13   0.306 -0.189  0.336
## soc_d_14R -0.101 -0.120  0.245
## soc_d_15R  0.149        -0.127
## soc_d_16   0.141              
## soc_d_17                      
## soc_d_18   0.207  0.270       
## soc_d_19R  0.178  0.510       
## soc_d_20         -0.135 -0.107
## soc_d_21   0.328 -0.286  0.329
## soc_d_22R         0.465       
## soc_d_23R -0.110              
## soc_d_24   0.108              
## soc_d_25  -0.131              
## soc_d_26   0.290  0.123  0.143
## soc_d_27   0.124        -0.747
## soc_d_28R        -0.164       
## soc_d_29          0.153 -0.171
## soc_d_30R         0.160       
## soc_d_31                      
## soc_d_32R                     
## soc_d_33                      
## 
##                 TC11  TC12   TC1   TC2   TC7   TC5   TC4  TC10   TC8   TC9
## SS loadings    1.655 1.626 1.600 1.586 1.579 1.538 1.511 1.507 1.460 1.425
## Proportion Var 0.050 0.049 0.048 0.048 0.048 0.047 0.046 0.046 0.044 0.043
## Cumulative Var 0.050 0.099 0.148 0.196 0.244 0.290 0.336 0.382 0.426 0.469
##                  TC3   TC6
## SS loadings    1.367 1.359
## Proportion Var 0.041 0.041
## Cumulative Var 0.511 0.552
## 
##           vars   n mean   sd median trimmed mad min max range  skew
## soc_d_1      1 262 0.67 0.47      1    0.71   0   0   1     1 -0.73
## soc_d_2      2 262 0.68 0.47      1    0.72   0   0   1     1 -0.75
## soc_d_3R     3 262 0.65 0.48      1    0.69   0   0   1     1 -0.64
## soc_d_4      4 262 0.21 0.41      0    0.14   0   0   1     1  1.42
## soc_d_5R     5 262 0.84 0.37      1    0.92   0   0   1     1 -1.80
## soc_d_6R     6 262 0.70 0.46      1    0.75   0   0   1     1 -0.86
## soc_d_7      7 262 0.49 0.50      0    0.49   0   0   1     1  0.03
## soc_d_8      8 262 0.53 0.50      1    0.54   0   0   1     1 -0.12
## soc_d_9R     9 262 0.53 0.50      1    0.54   0   0   1     1 -0.14
## soc_d_10R   10 262 0.73 0.45      1    0.79   0   0   1     1 -1.02
## soc_d_11R   11 262 0.66 0.47      1    0.70   0   0   1     1 -0.69
## soc_d_12R   12 262 0.51 0.50      1    0.51   0   0   1     1 -0.03
## soc_d_13    13 262 0.72 0.45      1    0.78   0   0   1     1 -0.98
## soc_d_14R   14 262 0.72 0.45      1    0.78   0   0   1     1 -0.98
## soc_d_15R   15 262 0.58 0.50      1    0.60   0   0   1     1 -0.31
## soc_d_16    16 262 0.63 0.48      1    0.67   0   0   1     1 -0.55
## soc_d_17    17 262 0.85 0.35      1    0.94   0   0   1     1 -2.00
## soc_d_18    18 262 0.26 0.44      0    0.20   0   0   1     1  1.11
## soc_d_19R   19 262 0.56 0.50      1    0.57   0   0   1     1 -0.23
## soc_d_20    20 262 0.79 0.41      1    0.86   0   0   1     1 -1.42
## soc_d_21    21 262 0.63 0.48      1    0.66   0   0   1     1 -0.53
## soc_d_22R   22 262 0.80 0.40      1    0.87   0   0   1     1 -1.47
## soc_d_23R   23 262 0.84 0.36      1    0.93   0   0   1     1 -1.88
## soc_d_24    24 262 0.82 0.38      1    0.90   0   0   1     1 -1.66
## soc_d_25    25 262 0.72 0.45      1    0.77   0   0   1     1 -0.96
## soc_d_26    26 262 0.40 0.49      0    0.37   0   0   1     1  0.42
## soc_d_27    27 262 0.51 0.50      1    0.51   0   0   1     1 -0.03
## soc_d_28R   28 262 0.84 0.36      1    0.93   0   0   1     1 -1.88
## soc_d_29    29 262 0.27 0.44      0    0.21   0   0   1     1  1.05
## soc_d_30R   30 262 0.58 0.49      1    0.60   0   0   1     1 -0.34
## soc_d_31    31 262 0.26 0.44      0    0.20   0   0   1     1  1.11
## soc_d_32R   32 262 0.47 0.50      0    0.47   0   0   1     1  0.11
## soc_d_33    33 262 0.30 0.46      0    0.25   0   0   1     1  0.88
##           kurtosis   se
## soc_d_1      -1.48 0.03
## soc_d_2      -1.45 0.03
## soc_d_3R     -1.60 0.03
## soc_d_4       0.01 0.03
## soc_d_5R      1.26 0.02
## soc_d_6R     -1.27 0.03
## soc_d_7      -2.01 0.03
## soc_d_8      -1.99 0.03
## soc_d_9R     -1.99 0.03
## soc_d_10R    -0.95 0.03
## soc_d_11R    -1.53 0.03
## soc_d_12R    -2.01 0.03
## soc_d_13     -1.04 0.03
## soc_d_14R    -1.04 0.03
## soc_d_15R    -1.91 0.03
## soc_d_16     -1.70 0.03
## soc_d_17      2.03 0.02
## soc_d_18     -0.76 0.03
## soc_d_19R    -1.95 0.03
## soc_d_20      0.01 0.03
## soc_d_21     -1.72 0.03
## soc_d_22R     0.17 0.02
## soc_d_23R     1.54 0.02
## soc_d_24      0.76 0.02
## soc_d_25     -1.08 0.03
## soc_d_26     -1.83 0.03
## soc_d_27     -2.01 0.03
## soc_d_28R     1.54 0.02
## soc_d_29     -0.91 0.03
## soc_d_30R    -1.89 0.03
## soc_d_31     -0.76 0.03
## soc_d_32R    -2.00 0.03
## soc_d_33     -1.23 0.03

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
soc_d_1	Social desirability questionnaire item 1: Before voting I thoroughly investigate the qualifications of all the candidates.	integer	0. No, 1. Yes	1	262	263	0.67	0.47	0	0	1	1	1	▃▁▁▁▁▁▁▇
soc_d_2	Social desirability questionnaire item 2: I never hesitate to go out of my way to help someone in trouble.	integer	0. No, 1. Yes	1	262	263	0.68	0.47	0	0	1	1	1	▃▁▁▁▁▁▁▇
soc_d_3R	Social desirability questionnaire item 3: It is sometimes hard for me to go on with my work if I am not encouraged.	numeric	0. No, 1. Yes	1	262	263	0.65	0.48	0	0	1	1	1	▅▁▁▁▁▁▁▇
soc_d_4	Social desirability questionnaire item 4: I have never intensely disliked anyone.	integer	0. No, 1. Yes	1	262	263	0.21	0.41	0	0	0	0	1	▇▁▁▁▁▁▁▂
soc_d_5R	Social desirability questionnaire item 5: On occasion I have had doubts about my ability to succeed in life.	numeric	0. No, 1. Yes	1	262	263	0.84	0.37	0	1	1	1	1	▂▁▁▁▁▁▁▇
soc_d_6R	Social desirability questionnaire item 6: I sometimes feel resentful when I don’t get my way.	numeric	0. No, 1. Yes	1	262	263	0.7	0.46	0	0	1	1	1	▃▁▁▁▁▁▁▇
soc_d_7	Social desirability questionnaire item 7: I am always careful about my manner of dress.	integer	0. No, 1. Yes	1	262	263	0.49	0.5	0	0	0	1	1	▇▁▁▁▁▁▁▇
soc_d_8	Social desirability questionnaire item 8: My table manners at home are as good as when I eat out in a restaurant.	integer	0. No, 1. Yes	1	262	263	0.53	0.5	0	0	1	1	1	▇▁▁▁▁▁▁▇
soc_d_9R	Social desirability questionnaire item 9: If I could get into a movie without paying and be sure I was not seen I would probably do it.	numeric	0. No, 1. Yes	1	262	263	0.53	0.5	0	0	1	1	1	▇▁▁▁▁▁▁▇
soc_d_10R	Social desirability questionnaire item 10: On a few occasions, I have given up doing something because I thought too little of my ability.	numeric	0. No, 1. Yes	1	262	263	0.73	0.45	0	0	1	1	1	▃▁▁▁▁▁▁▇
soc_d_11R	Social desirability questionnaire item 11: I like to gossip at times.	numeric	0. No, 1. Yes	1	262	263	0.66	0.47	0	0	1	1	1	▅▁▁▁▁▁▁▇
soc_d_12R	Social desirability questionnaire item 12: There have been times when I felt like rebelling against people in authority even though I knew they were right.	numeric	0. No, 1. Yes	1	262	263	0.51	0.5	0	0	1	1	1	▇▁▁▁▁▁▁▇
soc_d_13	Social desirability questionnaire item 13: No matter who I’m talking to, I’m always a good listener.	integer	0. No, 1. Yes	1	262	263	0.72	0.45	0	0	1	1	1	▃▁▁▁▁▁▁▇
soc_d_14R	Social desirability questionnaire item 14: I can remember ‘playing sick’ to get out of something.	numeric	0. No, 1. Yes	1	262	263	0.72	0.45	0	0	1	1	1	▃▁▁▁▁▁▁▇
soc_d_15R	Social desirability questionnaire item 15: There have been occasions when I took advantage of someone.	numeric	0. No, 1. Yes	1	262	263	0.58	0.5	0	0	1	1	1	▆▁▁▁▁▁▁▇
soc_d_16	Social desirability questionnaire item 16: I’m always willing to admit it when I make a mistake.	integer	0. No, 1. Yes	1	262	263	0.63	0.48	0	0	1	1	1	▅▁▁▁▁▁▁▇
soc_d_17	Social desirability questionnaire item 17: I always try to practice what I preach.	integer	0. No, 1. Yes	1	262	263	0.85	0.35	0	1	1	1	1	▂▁▁▁▁▁▁▇
soc_d_18	Social desirability questionnaire item 18: I don’t find it particularly difficult to get along with loud mouthed, obnoxious people.	integer	0. No, 1. Yes	1	262	263	0.26	0.44	0	0	0	1	1	▇▁▁▁▁▁▁▃
soc_d_19R	Social desirability questionnaire item 19: I sometimes try to get even rather than forgive and forget.	numeric	0. No, 1. Yes	1	262	263	0.56	0.5	0	0	1	1	1	▆▁▁▁▁▁▁▇
soc_d_20	Social desirability questionnaire item 20: When I don’t know something I don’t at all mind admitting it.	integer	0. No, 1. Yes	1	262	263	0.79	0.41	0	1	1	1	1	▂▁▁▁▁▁▁▇
soc_d_21	Social desirability questionnaire item 21: I am always courteous, even to people who are disagreeable.	integer	0. No, 1. Yes	1	262	263	0.63	0.48	0	0	1	1	1	▅▁▁▁▁▁▁▇
soc_d_22R	Social desirability questionnaire item 22: At times I have really insisted on having things my own way.	numeric	0. No, 1. Yes	1	262	263	0.8	0.4	0	1	1	1	1	▂▁▁▁▁▁▁▇
soc_d_23R	Social desirability questionnaire item 23: There have been occasions when I felt like smashing things.	numeric	0. No, 1. Yes	1	262	263	0.84	0.36	0	1	1	1	1	▂▁▁▁▁▁▁▇
soc_d_24	Social desirability questionnaire item 24: I would never think of letting someone else be punished for my wrong- doings.	integer	0. No, 1. Yes	1	262	263	0.82	0.38	0	1	1	1	1	▂▁▁▁▁▁▁▇
soc_d_25	Social desirability questionnaire item 25: I never resent being asked to return a favor.	integer	0. No, 1. Yes	1	262	263	0.72	0.45	0	0	1	1	1	▃▁▁▁▁▁▁▇
soc_d_26	Social desirability questionnaire item 26: I have never been irked when people expressed ideas very different from my own.	integer	0. No, 1. Yes	1	262	263	0.4	0.49	0	0	0	1	1	▇▁▁▁▁▁▁▅
soc_d_27	Social desirability questionnaire item 27: I never make a long trip without checking the safety of my car.	integer	0. No, 1. Yes	1	262	263	0.51	0.5	0	0	1	1	1	▇▁▁▁▁▁▁▇
soc_d_28R	Social desirability questionnaire item 28: There have been times when I was quite jealous of the good fortune of others.	numeric	0. No, 1. Yes	1	262	263	0.84	0.36	0	1	1	1	1	▂▁▁▁▁▁▁▇
soc_d_29	Social desirability questionnaire item 29: I have almost never felt the urge to tell someone off.	integer	0. No, 1. Yes	1	262	263	0.27	0.44	0	0	0	1	1	▇▁▁▁▁▁▁▃
soc_d_30R	Social desirability questionnaire item 30: I am sometimes irritated by people who ask favors of me.	numeric	0. No, 1. Yes	1	262	263	0.58	0.49	0	0	1	1	1	▆▁▁▁▁▁▁▇
soc_d_31	Social desirability questionnaire item 31: I have never felt that I was punished without cause.	integer	0. No, 1. Yes	1	262	263	0.26	0.44	0	0	0	1	1	▇▁▁▁▁▁▁▃
soc_d_32R	Social desirability questionnaire item 32: I sometimes think when people have a misfortune they only got what they deserved.	numeric	0. No, 1. Yes	1	262	263	0.47	0.5	0	0	0	1	1	▇▁▁▁▁▁▁▇
soc_d_33	Social desirability questionnaire item 33: I have never deliberately said something that hurt someone’s feelings.	integer	0. No, 1. Yes	1	262	263	0.3	0.46	0	0	0	1	1	▇▁▁▁▁▁▁▃

Scale: Careless responses

Overview

Reliability: ω_total [95% CI] = 0.14 [not computed].

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.1356
Omega Psych Tot	0.5682
Omega Psych H	0.2626
Cronbach Alpha	0.1181
Greatest Lower Bound	0.6327

Positive correlations: 12 out of 21 (57%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: cr_1R, cr_2, cr_3, cr_4, cr_5, cr_6, cr_7
##               Observations: 262
##      Positive correlations: 12 out of 21 (57%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.14
##       Omega (hierarchical): 0.26
##    Revelle's omega (total): 0.57
## Greatest Lower Bound (GLB): 0.63
##              Coefficient H: 1
##           Cronbach's alpha: 0.12
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): NA
##  Ordinal Omega (hierarch.): NA
##   Ordinal Cronbach's alpha: 0.62
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 1.791, 1.419, 1.102, 0.851, 0.786, 0.626, 0.426
## Loadings:
##       TC1    TC3    TC2   
## cr_1R  0.138 -0.801       
## cr_2          0.621  0.101
## cr_3   0.798 -0.155       
## cr_4         -0.126  0.805
## cr_5   0.546  0.483       
## cr_6   0.867              
## cr_7          0.122  0.774
## 
##                  TC1   TC3   TC2
## SS loadings    1.713 1.315 1.265
## Proportion Var 0.245 0.188 0.181
## Cumulative Var 0.245 0.433 0.613
## 
##       vars   n mean   sd median trimmed mad min max range  skew kurtosis
## cr_1R    1 262 0.91 0.29      1    1.00   0   0   1     1 -2.82     5.95
## cr_2     2 262 0.16 0.37      0    0.08   0   0   1     1  1.84     1.40
## cr_3     3 262 0.03 0.17      0    0.00   0   0   1     1  5.43    27.55
## cr_4     4 262 0.08 0.27      0    0.00   0   0   1     1  3.07     7.48
## cr_5     5 262 0.01 0.11      0    0.00   0   0   1     1  9.13    81.69
## cr_6     6 262 0.01 0.09      0    0.00   0   0   1     1 11.25   125.02
## cr_7     7 262 0.05 0.22      0    0.00   0   0   1     1  4.12    15.07
##         se
## cr_1R 0.02
## cr_2  0.02
## cr_3  0.01
## cr_4  0.02
## cr_5  0.01
## cr_6  0.01
## cr_7  0.01

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_1R	Careless response item 1: I am using an electronic device at this moment.	numeric	1. Yes, 0. No	1	262	263	0.91	0.29	0	1	1	1	1	▁▁▁▁▁▁▁▇
cr_2	Careless response item 2: I turn into a leprechaun at night.	integer	0. Yes, 1. No	1	262	263	0.16	0.37	0	0	0	0	1	▇▁▁▁▁▁▁▂
cr_3	Careless response item 3: All my friends are aliens.	integer	0. Yes, 1. No	1	262	263	0.031	0.17	0	0	0	0	1	▇▁▁▁▁▁▁▁
cr_4	Careless response item 4: All my friends say I would make a great poodle.	integer	0. Yes, 1. No	1	262	263	0.08	0.27	0	0	0	0	1	▇▁▁▁▁▁▁▁
cr_5	Careless response item 5: I eat cement occasionally.	integer	0. Yes, 1. No	1	262	263	0.011	0.11	0	0	0	0	1	▇▁▁▁▁▁▁▁
cr_6	Careless response item 6: I can teleport across time and space.	integer	0. Yes, 1. No	1	262	263	0.0076	0.087	0	0	0	0	1	▇▁▁▁▁▁▁▁
cr_7	Careless response item 7: I will be punished for meeting the requirements of my job.	integer	0. Yes, 1. No	1	262	263	0.05	0.22	0	0	0	0	1	▇▁▁▁▁▁▁▁

missingness_report

Missingness report

if (length(md_pattern)) {
  if (knitr::is_html_output()) {
    rmarkdown::paged_table(md_pattern, options = list(rows.print = 10))
  } else {
    knitr::kable(md_pattern)
  }
}

items

Codebook table

export_table(metadata_table)

jsonld

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Online (Prolific.co) data on TIPI, Social Desirability, Careless Response and Anchoring Paradigm, confidential data set",
  "description": "10 items taking from the Very brief measure of the Big 5 Personality questionnaire (Gosling, Rentfrow, & Swann, 2003), 33 items from the Social desirability scale (Crowne & Marlowe, 1960) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012). This dataset cannot be publicly shared, as the study consent for the participants represented in this data set stated that we would not provide public access to the data. If you are interested in re-analyzing the entire dataset, please contact the authors. Please find the shareable half of the data set on our osf.io page (see doi)\n\n\n## Table of variables\nThis table contains variable names, labels, their central tendencies and other attributes.\n\n|name                   |label                                                                                                                                                                                                                                            |data_type |value_labels                                                                                                                                                 |scale_item_names                                                                                                                                                                                                                                                                                                                               |missing |complete |n   |empty |n_unique |count |min |max |mean      |sd         |p0    |p25   |p50   |p75     |p100    |hist     |\n|:----------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------|:--------|:---|:-----|:--------|:-----|:---|:---|:---------|:----------|:-----|:-----|:-----|:-------|:-------|:--------|\n|V1                     |NA                                                                                                                                                                                                                                               |integer   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |NA    |NA       |NA    |NA  |NA  |297.31    |171.15     |1     |149.5 |300   |445     |576     |▇▆▇▆▇▇▇▇ |\n|id                     |ID variable from raw data                                                                                                                                                                                                                        |integer   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |NA    |NA       |NA    |NA  |NA  |328.89    |192.86     |1     |161.5 |330   |495     |654     |▇▇▇▇▇▇▇▇ |\n|consent                |Variables cb and ca combined in one variable                                                                                                                                                                                                     |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |0     |1        |NA    |1   |1   |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|cond_anc               |Anchoring condition: high and low                                                                                                                                                                                                                |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |0     |2        |NA    |1   |1   |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|refused                |Refusal to participate, one participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. This participant was in the 'no data sharing' condition. |integer   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |NA    |NA       |NA    |NA  |NA  |0.0038    |0.062      |0     |0     |0     |0       |1       |▇▁▁▁▁▁▁▁ |\n|remember               |Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy?                                                                                                                                           |integer   |0. No or wrong memory, - 1. correct memory                                                                                                                     |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.73      |0.44       |0     |0     |1     |1       |1       |▃▁▁▁▁▁▁▇ |\n|anc_baby               |Aggregated anchoring response, combining variables babieshigh and babieslow in one variable                                                                                                                                                      |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |77613.63  |4e+05      |1.8   |1000  |10000 |36750   |4e+06   |▇▁▁▁▁▁▁▁ |\n|anc_everest            |Aggregated anchoring response, combining variables everesthigh and everestlow in one variable                                                                                                                                                    |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |8981.49   |8176.08    |8     |6967  |8500  |9000    |84000   |▇▁▁▁▁▁▁▁ |\n|anc_chicago            |Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable                                                                                                                                                    |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |4.52      |6.27       |1e-04 |2     |3     |5       |50      |▇▁▁▁▁▁▁▁ |\n|gender_r               |Gender variable cleaned for grammar, language variations and orthography                                                                                                                                                                         |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |3       |260      |263 |0     |3        |NA    |4   |10  |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|oq                     |NA                                                                                                                                                                                                                                               |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |33      |230      |263 |0     |227      |NA    |2   |897 |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|bf_1                   |TIPI item 1, Extraversion: I see myself as extraverted, enthousiastic.                                                                                                                                                                           |integer   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |3.77      |1.79       |1     |2     |4     |5       |7       |▅▆▇▅▁▇▅▂ |\n|bf_2R                  |TIPI item 2, Agreeableness: I see myself as critical, quarrelsome.                                                                                                                                                                               |numeric   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |4.23      |1.61       |1     |3     |5     |5       |7       |▂▃▃▃▁▇▅▁ |\n|bf_3                   |TIPI item 3, Conscientiousness: I see myself as dependable, self-disciplined.                                                                                                                                                                    |integer   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |5.03      |1.4        |1     |4     |5     |6       |7       |▁▁▂▃▁▇▇▃ |\n|bf_5R                  |TIPI item 4, Neuroticsm: I see myself as anxious, easily upset.                                                                                                                                                                                  |numeric   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |3.98      |1.82       |1     |2     |4     |5       |7       |▂▇▆▃▁▇▅▃ |\n|bf_6                   |TIPI item 5, Openness to experience: I see myself as open to new experiences, complex.                                                                                                                                                           |integer   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |5.29      |1.22       |1     |5     |5     |6       |7       |▁▁▁▂▁▇▆▃ |\n|bf_7R                  |TIPI item 6, Extraversion: I see myself as reserved, quiet.                                                                                                                                                                                      |numeric   |7. Disagree strongly, - 6. Disagree moderately, - 5. Disagree a little, - 4. Neither agree nor disagree, - 3. Agree a little, - 2. Agree moderately, - 1. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |3.38      |1.73       |1     |2     |3     |5       |7       |▅▆▇▂▁▅▃▂ |\n|bf_8                   |TIPI item 7, Agreeableness: I see myself as sympathetic, warm.                                                                                                                                                                                   |integer   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |5.32      |1.34       |1     |5     |6     |6       |7       |▁▁▁▂▁▇▇▃ |\n|bf_10R                 |TIPI item 8, Conscientiousness: I see myself as disorganized, careless.                                                                                                                                                                          |numeric   |7. Disagree strongly, - 6. Disagree moderately, - 5. Disagree a little, - 4. Neither agree nor disagree, - 3. Agree a little, - 2. Agree moderately, - 1. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |5.05      |1.59       |1     |4     |5     |6       |7       |▁▂▅▃▁▅▇▆ |\n|bf_11                  |TIPI item 9, Neuroticsm: I see myself as calm, emotionally stable.                                                                                                                                                                               |integer   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |4.68      |1.54       |1     |4     |5     |6       |7       |▁▂▅▅▁▇▇▃ |\n|bf_12R                 |TIPI item 10, Openness to experience: I see myself as conventional, uncreative.                                                                                                                                                                  |numeric   |7. Disagree strongly, - 6. Disagree moderately, - 5. Disagree a little, - 4. Neither agree nor disagree, - 3. Agree a little, - 2. Agree moderately, - 1. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |4.79      |1.51       |1     |4     |5     |6       |7       |▁▂▃▅▁▆▇▃ |\n|soc_d_1                |Social desirability questionnaire item 1: Before voting I thoroughly investigate the qualifications of all the candidates.                                                                                                                       |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.67      |0.47       |0     |0     |1     |1       |1       |▃▁▁▁▁▁▁▇ |\n|soc_d_2                |Social desirability questionnaire item 2: I never hesitate to go out of my way to help someone in trouble.                                                                                                                                       |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.68      |0.47       |0     |0     |1     |1       |1       |▃▁▁▁▁▁▁▇ |\n|soc_d_3R               |Social desirability questionnaire item 3: It is sometimes hard for me to go on with my work if I am not encouraged.                                                                                                                              |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.65      |0.48       |0     |0     |1     |1       |1       |▅▁▁▁▁▁▁▇ |\n|soc_d_4                |Social desirability questionnaire item 4: I have never intensely disliked anyone.                                                                                                                                                                |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.21      |0.41       |0     |0     |0     |0       |1       |▇▁▁▁▁▁▁▂ |\n|soc_d_5R               |Social desirability questionnaire item 5: On occasion I have had doubts about my ability to succeed in life.                                                                                                                                     |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.84      |0.37       |0     |1     |1     |1       |1       |▂▁▁▁▁▁▁▇ |\n|soc_d_6R               |Social desirability questionnaire item 6: I sometimes feel resentful when I don't get my way.                                                                                                                                                    |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.7       |0.46       |0     |0     |1     |1       |1       |▃▁▁▁▁▁▁▇ |\n|soc_d_7                |Social desirability questionnaire item 7: I am always careful about my manner of dress.                                                                                                                                                          |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.49      |0.5        |0     |0     |0     |1       |1       |▇▁▁▁▁▁▁▇ |\n|soc_d_8                |Social desirability questionnaire item 8: My table manners at home are as good as when I eat out in a restaurant.                                                                                                                                |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.53      |0.5        |0     |0     |1     |1       |1       |▇▁▁▁▁▁▁▇ |\n|soc_d_9R               |Social desirability questionnaire item 9: If I could get into a movie without paying and be sure I was not seen I would probably do it.                                                                                                          |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.53      |0.5        |0     |0     |1     |1       |1       |▇▁▁▁▁▁▁▇ |\n|soc_d_10R              |Social desirability questionnaire item 10: On a few occasions, I have given up doing something because I thought too little of my ability.                                                                                                       |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.73      |0.45       |0     |0     |1     |1       |1       |▃▁▁▁▁▁▁▇ |\n|soc_d_11R              |Social desirability questionnaire item 11: I like to gossip at times.                                                                                                                                                                            |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.66      |0.47       |0     |0     |1     |1       |1       |▅▁▁▁▁▁▁▇ |\n|soc_d_12R              |Social desirability questionnaire item 12: There have been times when I felt like rebelling against people in authority even though I knew they were right.                                                                                      |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.51      |0.5        |0     |0     |1     |1       |1       |▇▁▁▁▁▁▁▇ |\n|soc_d_13               |Social desirability questionnaire item 13: No matter who I'm talking to, I'm always a good listener.                                                                                                                                             |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.72      |0.45       |0     |0     |1     |1       |1       |▃▁▁▁▁▁▁▇ |\n|soc_d_14R              |Social desirability questionnaire item 14: I can remember 'playing sick' to get out of something.                                                                                                                                                |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.72      |0.45       |0     |0     |1     |1       |1       |▃▁▁▁▁▁▁▇ |\n|soc_d_15R              |Social desirability questionnaire item 15: There have been occasions when I took advantage of someone.                                                                                                                                           |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.58      |0.5        |0     |0     |1     |1       |1       |▆▁▁▁▁▁▁▇ |\n|soc_d_16               |Social desirability questionnaire item 16: I'm always willing to admit it when I make a mistake.                                                                                                                                                 |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.63      |0.48       |0     |0     |1     |1       |1       |▅▁▁▁▁▁▁▇ |\n|soc_d_17               |Social desirability questionnaire item 17: I always try to practice what I preach.                                                                                                                                                               |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.85      |0.35       |0     |1     |1     |1       |1       |▂▁▁▁▁▁▁▇ |\n|soc_d_18               |Social desirability questionnaire item 18: I don't find it particularly difficult to get along with loud mouthed, obnoxious people.                                                                                                              |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.26      |0.44       |0     |0     |0     |1       |1       |▇▁▁▁▁▁▁▃ |\n|soc_d_19R              |Social desirability questionnaire item 19: I sometimes try to get even rather than forgive and forget.                                                                                                                                           |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.56      |0.5        |0     |0     |1     |1       |1       |▆▁▁▁▁▁▁▇ |\n|soc_d_20               |Social desirability questionnaire item 20: When I don't know something I don't at all mind admitting it.                                                                                                                                         |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.79      |0.41       |0     |1     |1     |1       |1       |▂▁▁▁▁▁▁▇ |\n|soc_d_21               |Social desirability questionnaire item 21: I am always courteous, even to people who are disagreeable.                                                                                                                                           |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.63      |0.48       |0     |0     |1     |1       |1       |▅▁▁▁▁▁▁▇ |\n|soc_d_22R              |Social desirability questionnaire item 22: At times I have really insisted on having things my own way.                                                                                                                                          |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.8       |0.4        |0     |1     |1     |1       |1       |▂▁▁▁▁▁▁▇ |\n|soc_d_23R              |Social desirability questionnaire item 23: There have been occasions when I felt like smashing things.                                                                                                                                           |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.84      |0.36       |0     |1     |1     |1       |1       |▂▁▁▁▁▁▁▇ |\n|soc_d_24               |Social desirability questionnaire item 24: I would never think of letting someone else be punished for my wrong- doings.                                                                                                                         |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.82      |0.38       |0     |1     |1     |1       |1       |▂▁▁▁▁▁▁▇ |\n|soc_d_25               |Social desirability questionnaire item 25: I never resent being asked to return a favor.                                                                                                                                                         |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.72      |0.45       |0     |0     |1     |1       |1       |▃▁▁▁▁▁▁▇ |\n|soc_d_26               |Social desirability questionnaire item 26: I have never been irked when people expressed ideas very different from my own.                                                                                                                       |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.4       |0.49       |0     |0     |0     |1       |1       |▇▁▁▁▁▁▁▅ |\n|soc_d_27               |Social desirability questionnaire item 27: I never make a long trip without checking the safety of my car.                                                                                                                                       |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.51      |0.5        |0     |0     |1     |1       |1       |▇▁▁▁▁▁▁▇ |\n|soc_d_28R              |Social desirability questionnaire item 28: There have been times when I was quite jealous of the good fortune of others.                                                                                                                         |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.84      |0.36       |0     |1     |1     |1       |1       |▂▁▁▁▁▁▁▇ |\n|soc_d_29               |Social desirability questionnaire item 29: I have almost never felt the urge to tell someone off.                                                                                                                                                |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.27      |0.44       |0     |0     |0     |1       |1       |▇▁▁▁▁▁▁▃ |\n|soc_d_30R              |Social desirability questionnaire item 30: I am sometimes irritated by people who ask favors of me.                                                                                                                                              |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.58      |0.49       |0     |0     |1     |1       |1       |▆▁▁▁▁▁▁▇ |\n|soc_d_31               |Social desirability questionnaire item 31: I have never felt that I was punished without cause.                                                                                                                                                  |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.26      |0.44       |0     |0     |0     |1       |1       |▇▁▁▁▁▁▁▃ |\n|soc_d_32R              |Social desirability questionnaire item 32: I sometimes think when people have a misfortune they only got what they deserved.                                                                                                                     |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.47      |0.5        |0     |0     |0     |1       |1       |▇▁▁▁▁▁▁▇ |\n|soc_d_33               |Social desirability questionnaire item 33: I have never deliberately said something that hurt someone's feelings.                                                                                                                                |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.3       |0.46       |0     |0     |0     |1       |1       |▇▁▁▁▁▁▁▃ |\n|cr_1R                  |Careless response item 1: I am using an electronic device at this moment.                                                                                                                                                                        |numeric   |1. Yes, - 0. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.91      |0.29       |0     |1     |1     |1       |1       |▁▁▁▁▁▁▁▇ |\n|cr_2                   |Careless response item 2: I turn into a leprechaun at night.                                                                                                                                                                                     |integer   |0. Yes, - 1. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.16      |0.37       |0     |0     |0     |0       |1       |▇▁▁▁▁▁▁▂ |\n|cr_3                   |Careless response item 3: All my friends are aliens.                                                                                                                                                                                             |integer   |0. Yes, - 1. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.031     |0.17       |0     |0     |0     |0       |1       |▇▁▁▁▁▁▁▁ |\n|cr_4                   |Careless response item 4: All my friends say I would make a great poodle.                                                                                                                                                                        |integer   |0. Yes, - 1. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.08      |0.27       |0     |0     |0     |0       |1       |▇▁▁▁▁▁▁▁ |\n|cr_5                   |Careless response item 5: I eat cement occasionally.                                                                                                                                                                                             |integer   |0. Yes, - 1. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.011     |0.11       |0     |0     |0     |0       |1       |▇▁▁▁▁▁▁▁ |\n|cr_6                   |Careless response item 6: I can teleport across time and space.                                                                                                                                                                                  |integer   |0. Yes, - 1. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.0076    |0.087      |0     |0     |0     |0       |1       |▇▁▁▁▁▁▁▁ |\n|cr_7                   |Careless response item 7: I will be punished for meeting the requirements of my job.                                                                                                                                                             |integer   |0. Yes, - 1. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.05      |0.22       |0     |0     |0     |0       |1       |▇▁▁▁▁▁▁▁ |\n|everesthigh            |Anchoring paradigm, high anchor: Height of Mount Everest                                                                                                                                                                                         |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |137     |126      |263 |NA    |NA       |NA    |NA  |NA  |11573.93  |9024.16    |8.85  |8650  |8900  |13337.5 |84000   |▇▃▁▁▁▁▁▁ |\n|chicagohigh            |Anchoring paradigm, high anchor: Population of Chicago                                                                                                                                                                                           |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |137     |126      |263 |NA    |NA       |NA    |NA  |NA  |313337.14 |1153850.61 |1.3   |3     |3     |7       |7e+06   |▇▁▁▁▁▁▁▁ |\n|babieshigh             |Anchoring paradigm, high anchor: Babies born each day                                                                                                                                                                                            |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |137     |126      |263 |NA    |NA       |NA    |NA  |NA  |110755.6  |455097.97  |1.8   |10000 |30000 |50000   |4e+06   |▇▁▁▁▁▁▁▁ |\n|everestlow             |Anchoring paradigm, low anchor: Height of Mount Everest                                                                                                                                                                                          |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |127     |136      |263 |NA    |NA       |NA    |NA  |NA  |6579.68   |6461.63    |8     |2000  |8000  |8533    |60000   |▇▇▁▁▁▁▁▁ |\n|chicagolow             |Anchoring paradigm, low anchor: Population of Chicago                                                                                                                                                                                            |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |127     |136      |263 |NA    |NA       |NA    |NA  |NA  |409740.82 |4297754.77 |0.1   |1.2   |2.6   |5.05    |5e+07   |▇▁▁▁▁▁▁▁ |\n|babieslow              |Anchoring paradgim, low anchor: Babies born each day                                                                                                                                                                                             |integer   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |127     |136      |263 |NA    |NA       |NA    |NA  |NA  |46908.57  |341493.94  |10    |230   |1000  |10000   |3853000 |▇▁▁▁▁▁▁▁ |\n|d1                     |NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained?                                                            |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |0     |3        |NA    |2   |3   |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|d2.sq001               |NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : Yes, I remember                                                                     |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |0     |3        |NA    |2   |3   |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|d2.sq002               |NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : No, I don't remember                                                                |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |0     |3        |NA    |2   |3   |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|d3.sq001               |NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': Yes                                                                                     |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |0     |3        |NA    |2   |3   |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|d3.sq002               |NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': No                                                                                      |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |0     |3        |NA    |2   |3   |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|d3.sq003               |NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': I don't remember                                                                        |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |0     |3        |NA    |2   |3   |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|gender                 |Gender: open-entry self-report                                                                                                                                                                                                                   |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |2       |261      |263 |0     |11       |NA    |1   |11  |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|age                    |Age categories                                                                                                                                                                                                                                   |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |2       |261      |263 |0     |8        |NA    |13  |13  |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|end                    |NA                                                                                                                                                                                                                                               |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |237     |26       |263 |0     |26       |NA    |1   |228 |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|return                 |NA                                                                                                                                                                                                                                               |logical   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |263     |0        |263 |NA    |NA       |263   |NA  |NA  |NaN       |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|lastpage               |Last page completed by the participant, page 12 and 13 are considered as full participation                                                                                                                                                      |integer   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |NA    |NA       |NA    |NA  |NA  |12.19     |0.39       |12    |12    |12    |12      |13      |▇▁▁▁▁▁▁▂ |\n|random                 |Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor.                                                                                                          |integer   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |NA    |NA       |NA    |NA  |NA  |1.52      |0.5        |1     |1     |2     |2       |2       |▇▁▁▁▁▁▁▇ |\n|cb                     |No data sharing policy consent presented. One participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. See manuscript for details             |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |263      |263 |0     |2        |NA    |7   |10  |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|ca                     |Data sharing policy presented                                                                                                                                                                                                                    |logical   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |263     |0        |263 |NA    |NA       |263   |NA  |NA  |NaN       |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|mc_1                   |comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes?                                                                                                                                                     |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |0     |3        |NA    |2   |16  |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|mc_2                   |comprehension question consent 2 (distractor): Is your data anonymous?                                                                                                                                                                           |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |0     |2        |NA    |2   |3   |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|mc_3                   |comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared?                                                                                                              |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |0     |3        |NA    |2   |16  |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|mc_4                   |comprehension question consent 3 (distractor): Can you stop your participation at any time?                                                                                                                                                      |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |1       |262      |263 |0     |3        |NA    |2   |16  |NA        |NA         |NA    |NA    |NA    |NA      |NA      |NA       |\n|Extraversion           |2 bf items aggregated by rowMeans                                                                                                                                                                                                                |numeric   |NA                                                                                                                                                           |bf_1, bf_7R                                                                                                                                                                                                                                                                                                                                    |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |3.57      |1.56       |1     |2.5   |3.5   |4.88    |7       |▅▇▃▆▃▅▂▂ |\n|Agreeableness          |2 bf items aggregated by rowMeans                                                                                                                                                                                                                |numeric   |NA                                                                                                                                                           |bf_2R, bf_8                                                                                                                                                                                                                                                                                                                                    |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |4.54      |1.13       |1     |4     |4.5   |5       |7       |▁▁▁▇▆▆▂▂ |\n|Conscientiousness      |2 bf items aggregated by rowMeans                                                                                                                                                                                                                |numeric   |NA                                                                                                                                                           |bf_3, bf_10R                                                                                                                                                                                                                                                                                                                                   |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |5.04      |1.25       |1.5   |4.5   |5     |6       |7       |▁▁▂▂▅▇▅▅ |\n|Neuroticism            |2 bf items aggregated by rowMeans                                                                                                                                                                                                                |numeric   |NA                                                                                                                                                           |bf_5R, bf_11                                                                                                                                                                                                                                                                                                                                   |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |4.35      |1.48       |1     |3     |4.5   |5.5     |7       |▁▃▃▇▂▇▃▃ |\n|Openness to experience |2 bf items aggregated by rowMeans                                                                                                                                                                                                                |numeric   |NA                                                                                                                                                           |bf_6, bf_12R                                                                                                                                                                                                                                                                                                                                   |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |5.04      |1.1        |1.5   |4.5   |5     |6       |7       |▁▁▂▂▃▇▃▃ |\n|Social Desirability    |33 soc_d items aggregated by rowMeans                                                                                                                                                                                                            |numeric   |NA                                                                                                                                                           |soc_d_1, soc_d_2, soc_d_3R, soc_d_4, soc_d_5R, soc_d_6R, soc_d_7, soc_d_8, soc_d_9R, soc_d_10R, soc_d_11R, soc_d_12R, soc_d_13, soc_d_14R, soc_d_15R, soc_d_16, soc_d_17, soc_d_18, soc_d_19R, soc_d_20, soc_d_21, soc_d_22R, soc_d_23R, soc_d_24, soc_d_25, soc_d_26, soc_d_27, soc_d_28R, soc_d_29, soc_d_30R, soc_d_31, soc_d_32R, soc_d_33 |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.45      |0.14       |0.12  |0.33  |0.42  |0.55    |0.88    |▂▃▇▆▅▃▂▁ |\n|Careless responses     |7 cr items aggregated by rowMeans                                                                                                                                                                                                                |numeric   |NA                                                                                                                                                           |cr_1R, cr_2, cr_3, cr_4, cr_5, cr_6, cr_7                                                                                                                                                                                                                                                                                                      |1       |262      |263 |NA    |NA       |NA    |NA  |NA  |0.18      |0.094      |0     |0.14  |0.14  |0.14    |0.57    |▁▇▁▂▁▁▁▁ |\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.8.1).",
  "identifier": "https://doi.org/10.17605/OSF.IO/AM6BC",
  "creator": "Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein",
  "citation": "Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Data collected online. https://doi.org/10.17605/OSF.IO/AM6BC",
  "datePublished": "2019-08-06",
  "temporalCoverage": "2019-06-17 to 2019-06-21",
  "spatialCoverage": "Online participants residing in, or citizens of, the EU at time of data collection",
  "keywords": ["V1", "id", "consent", "cond_anc", "refused", "remember", "anc_baby", "anc_everest", "anc_chicago", "gender_r", "oq", "bf_1", "bf_2R", "bf_3", "bf_5R", "bf_6", "bf_7R", "bf_8", "bf_10R", "bf_11", "bf_12R", "soc_d_1", "soc_d_2", "soc_d_3R", "soc_d_4", "soc_d_5R", "soc_d_6R", "soc_d_7", "soc_d_8", "soc_d_9R", "soc_d_10R", "soc_d_11R", "soc_d_12R", "soc_d_13", "soc_d_14R", "soc_d_15R", "soc_d_16", "soc_d_17", "soc_d_18", "soc_d_19R", "soc_d_20", "soc_d_21", "soc_d_22R", "soc_d_23R", "soc_d_24", "soc_d_25", "soc_d_26", "soc_d_27", "soc_d_28R", "soc_d_29", "soc_d_30R", "soc_d_31", "soc_d_32R", "soc_d_33", "cr_1R", "cr_2", "cr_3", "cr_4", "cr_5", "cr_6", "cr_7", "everesthigh", "chicagohigh", "babieshigh", "everestlow", "chicagolow", "babieslow", "d1", "d2.sq001", "d2.sq002", "d3.sq001", "d3.sq002", "d3.sq003", "gender", "age", "end", "return", "lastpage", "random", "cb", "ca", "mc_1", "mc_2", "mc_3", "mc_4", "Extraversion", "Agreeableness", "Conscientiousness", "Neuroticism", "Openness to experience", "Social Desirability", "Careless responses"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "V1",
      "@type": "propertyValue"
    },
    {
      "name": "id",
      "description": "ID variable from raw data",
      "@type": "propertyValue"
    },
    {
      "name": "consent",
      "description": "Variables cb and ca combined in one variable",
      "@type": "propertyValue"
    },
    {
      "name": "cond_anc",
      "description": "Anchoring condition: high and low",
      "@type": "propertyValue"
    },
    {
      "name": "refused",
      "description": "Refusal to participate, one participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. This participant was in the 'no data sharing' condition.",
      "@type": "propertyValue"
    },
    {
      "name": "remember",
      "description": "Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy?",
      "value": "0. No or wrong memory,\n1. correct memory",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "anc_baby",
      "description": "Aggregated anchoring response, combining variables babieshigh and babieslow in one variable",
      "@type": "propertyValue"
    },
    {
      "name": "anc_everest",
      "description": "Aggregated anchoring response, combining variables everesthigh and everestlow in one variable",
      "@type": "propertyValue"
    },
    {
      "name": "anc_chicago",
      "description": "Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable",
      "@type": "propertyValue"
    },
    {
      "name": "gender_r",
      "description": "Gender variable cleaned for grammar, language variations and orthography",
      "@type": "propertyValue"
    },
    {
      "name": "oq",
      "@type": "propertyValue"
    },
    {
      "name": "bf_1",
      "description": "TIPI item 1, Extraversion: I see myself as extraverted, enthousiastic.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_2R",
      "description": "TIPI item 2, Agreeableness: I see myself as critical, quarrelsome.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_3",
      "description": "TIPI item 3, Conscientiousness: I see myself as dependable, self-disciplined.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_5R",
      "description": "TIPI item 4, Neuroticsm: I see myself as anxious, easily upset.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_6",
      "description": "TIPI item 5, Openness to experience: I see myself as open to new experiences, complex.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_7R",
      "description": "TIPI item 6, Extraversion: I see myself as reserved, quiet.",
      "value": "7. Disagree strongly,\n6. Disagree moderately,\n5. Disagree a little,\n4. Neither agree nor disagree,\n3. Agree a little,\n2. Agree moderately,\n1. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_8",
      "description": "TIPI item 7, Agreeableness: I see myself as sympathetic, warm.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_10R",
      "description": "TIPI item 8, Conscientiousness: I see myself as disorganized, careless.",
      "value": "7. Disagree strongly,\n6. Disagree moderately,\n5. Disagree a little,\n4. Neither agree nor disagree,\n3. Agree a little,\n2. Agree moderately,\n1. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_11",
      "description": "TIPI item 9, Neuroticsm: I see myself as calm, emotionally stable.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_12R",
      "description": "TIPI item 10, Openness to experience: I see myself as conventional, uncreative.",
      "value": "7. Disagree strongly,\n6. Disagree moderately,\n5. Disagree a little,\n4. Neither agree nor disagree,\n3. Agree a little,\n2. Agree moderately,\n1. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_1",
      "description": "Social desirability questionnaire item 1: Before voting I thoroughly investigate the qualifications of all the candidates.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_2",
      "description": "Social desirability questionnaire item 2: I never hesitate to go out of my way to help someone in trouble.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_3R",
      "description": "Social desirability questionnaire item 3: It is sometimes hard for me to go on with my work if I am not encouraged.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_4",
      "description": "Social desirability questionnaire item 4: I have never intensely disliked anyone.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_5R",
      "description": "Social desirability questionnaire item 5: On occasion I have had doubts about my ability to succeed in life.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_6R",
      "description": "Social desirability questionnaire item 6: I sometimes feel resentful when I don't get my way.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_7",
      "description": "Social desirability questionnaire item 7: I am always careful about my manner of dress.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_8",
      "description": "Social desirability questionnaire item 8: My table manners at home are as good as when I eat out in a restaurant.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_9R",
      "description": "Social desirability questionnaire item 9: If I could get into a movie without paying and be sure I was not seen I would probably do it.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_10R",
      "description": "Social desirability questionnaire item 10: On a few occasions, I have given up doing something because I thought too little of my ability.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_11R",
      "description": "Social desirability questionnaire item 11: I like to gossip at times.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_12R",
      "description": "Social desirability questionnaire item 12: There have been times when I felt like rebelling against people in authority even though I knew they were right.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_13",
      "description": "Social desirability questionnaire item 13: No matter who I'm talking to, I'm always a good listener.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_14R",
      "description": "Social desirability questionnaire item 14: I can remember 'playing sick' to get out of something.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_15R",
      "description": "Social desirability questionnaire item 15: There have been occasions when I took advantage of someone.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_16",
      "description": "Social desirability questionnaire item 16: I'm always willing to admit it when I make a mistake.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_17",
      "description": "Social desirability questionnaire item 17: I always try to practice what I preach.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_18",
      "description": "Social desirability questionnaire item 18: I don't find it particularly difficult to get along with loud mouthed, obnoxious people.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_19R",
      "description": "Social desirability questionnaire item 19: I sometimes try to get even rather than forgive and forget.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_20",
      "description": "Social desirability questionnaire item 20: When I don't know something I don't at all mind admitting it.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_21",
      "description": "Social desirability questionnaire item 21: I am always courteous, even to people who are disagreeable.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_22R",
      "description": "Social desirability questionnaire item 22: At times I have really insisted on having things my own way.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_23R",
      "description": "Social desirability questionnaire item 23: There have been occasions when I felt like smashing things.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_24",
      "description": "Social desirability questionnaire item 24: I would never think of letting someone else be punished for my wrong- doings.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_25",
      "description": "Social desirability questionnaire item 25: I never resent being asked to return a favor.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_26",
      "description": "Social desirability questionnaire item 26: I have never been irked when people expressed ideas very different from my own.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_27",
      "description": "Social desirability questionnaire item 27: I never make a long trip without checking the safety of my car.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_28R",
      "description": "Social desirability questionnaire item 28: There have been times when I was quite jealous of the good fortune of others.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_29",
      "description": "Social desirability questionnaire item 29: I have almost never felt the urge to tell someone off.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_30R",
      "description": "Social desirability questionnaire item 30: I am sometimes irritated by people who ask favors of me. ",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_31",
      "description": "Social desirability questionnaire item 31: I have never felt that I was punished without cause.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_32R",
      "description": "Social desirability questionnaire item 32: I sometimes think when people have a misfortune they only got what they deserved.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_33",
      "description": "Social desirability questionnaire item 33: I have never deliberately said something that hurt someone's feelings.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_1R",
      "description": "Careless response item 1: I am using an electronic device at this moment.",
      "value": "1. Yes,\n0. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_2",
      "description": "Careless response item 2: I turn into a leprechaun at night.",
      "value": "0. Yes,\n1. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_3",
      "description": "Careless response item 3: All my friends are aliens.",
      "value": "0. Yes,\n1. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_4",
      "description": "Careless response item 4: All my friends say I would make a great poodle.",
      "value": "0. Yes,\n1. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_5",
      "description": "Careless response item 5: I eat cement occasionally.",
      "value": "0. Yes,\n1. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_6",
      "description": "Careless response item 6: I can teleport across time and space.",
      "value": "0. Yes,\n1. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_7",
      "description": "Careless response item 7: I will be punished for meeting the requirements of my job.",
      "value": "0. Yes,\n1. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "everesthigh",
      "description": "Anchoring paradigm, high anchor: Height of Mount Everest",
      "@type": "propertyValue"
    },
    {
      "name": "chicagohigh",
      "description": "Anchoring paradigm, high anchor: Population of Chicago",
      "@type": "propertyValue"
    },
    {
      "name": "babieshigh",
      "description": "Anchoring paradigm, high anchor: Babies born each day",
      "@type": "propertyValue"
    },
    {
      "name": "everestlow",
      "description": "Anchoring paradigm, low anchor: Height of Mount Everest",
      "@type": "propertyValue"
    },
    {
      "name": "chicagolow",
      "description": "Anchoring paradigm, low anchor: Population of Chicago",
      "@type": "propertyValue"
    },
    {
      "name": "babieslow",
      "description": "Anchoring paradgim, low anchor: Babies born each day",
      "@type": "propertyValue"
    },
    {
      "name": "d1",
      "description": "NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained?",
      "@type": "propertyValue"
    },
    {
      "name": "d2.sq001",
      "description": "NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : Yes, I remember",
      "@type": "propertyValue"
    },
    {
      "name": "d2.sq002",
      "description": "NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : No, I don't remember",
      "@type": "propertyValue"
    },
    {
      "name": "d3.sq001",
      "description": "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': Yes\n",
      "@type": "propertyValue"
    },
    {
      "name": "d3.sq002",
      "description": "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': No",
      "@type": "propertyValue"
    },
    {
      "name": "d3.sq003",
      "description": "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': I don't remember",
      "@type": "propertyValue"
    },
    {
      "name": "gender",
      "description": "Gender: open-entry self-report",
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "description": "Age categories",
      "@type": "propertyValue"
    },
    {
      "name": "end",
      "@type": "propertyValue"
    },
    {
      "name": "return",
      "@type": "propertyValue"
    },
    {
      "name": "lastpage",
      "description": "Last page completed by the participant, page 12 and 13 are considered as full participation",
      "@type": "propertyValue"
    },
    {
      "name": "random",
      "description": "Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor.",
      "@type": "propertyValue"
    },
    {
      "name": "cb",
      "description": "No data sharing policy consent presented. One participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. See manuscript for details",
      "@type": "propertyValue"
    },
    {
      "name": "ca",
      "description": "Data sharing policy presented",
      "@type": "propertyValue"
    },
    {
      "name": "mc_1",
      "description": "comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes?",
      "@type": "propertyValue"
    },
    {
      "name": "mc_2",
      "description": "comprehension question consent 2 (distractor): Is your data anonymous?",
      "@type": "propertyValue"
    },
    {
      "name": "mc_3",
      "description": "comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared?",
      "@type": "propertyValue"
    },
    {
      "name": "mc_4",
      "description": "comprehension question consent 3 (distractor): Can you stop your participation at any time?",
      "@type": "propertyValue"
    },
    {
      "name": "Extraversion",
      "description": "2 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Agreeableness",
      "description": "2 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Conscientiousness",
      "description": "2 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Neuroticism",
      "description": "2 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Openness to experience",
      "description": "2 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Social Desirability",
      "description": "33 soc_d items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Careless responses",
      "description": "7 cr items aggregated by rowMeans",
      "@type": "propertyValue"
    }
  ]
}`